Wang Qichen, Zhao Fubo, Wang Xi, Shi Wenbo, Shan Yinuo, Li Qiang, Liu Dengfeng, Wu Yiping
Institute of Global Environmental Change, Xi'an Jiaotong University, Xi'an, Shaanxi Province, 710049, China.
College of Forestry, Northwest A&F University, Yangling, 712100, China.
Sci Data. 2025 Jun 19;12(1):1032. doi: 10.1038/s41597-025-05389-8.
A comprehensive and long-term dataset of prefectural surface water resources is crucial for effective water resources management in China. However, there has been a significant gap in the availability of such datasets, with no existing datasets providing comprehensive long-term coverage. To address this gap, we have developed CNSW 1.0, the first long-term (2000-2020) dataset of prefectural surface water resources in China. Utilizing surface water resources data from official water resources bulletins, we employed 14 advanced machine learning models to reconstruct the CNSW 1.0 dataset. The resulting dataset exhibits high accuracy, with an R of 0.98 for total surface water resources and acceptable level of bias across China. CNSW 1.0 not only outperforms existing datasets like CNRD v1.0, GRUN, and ISIMIP in terms of simulation accuracy and spatial distribution but also fills a critical gap in water resources data for China. This dataset is expected to be an invaluable tool for developing more informed water resources management strategies at the administrative level in China, particularly in the context of climate change.
一个全面且长期的县级地表水资源数据集对于中国有效的水资源管理至关重要。然而，此类数据集的可用性存在显著差距，目前没有现有数据集能提供全面的长期覆盖。为了填补这一差距，我们开发了CNSW 1.0，这是中国首个县级地表水资源长期（2000 - 2020年）数据集。利用官方水资源公报中的地表水资源数据，我们采用了14种先进的机器学习模型来重建CNSW 1.0数据集。所得数据集显示出高精度，总地表水资源的R值为0.98，且在中国各地的偏差水平可接受。CNSW 1.0不仅在模拟精度和空间分布方面优于现有数据集，如CNRD v1.0、GRUN和ISIMIP，还填补了中国水资源数据的关键空白。预计该数据集将成为中国行政层面制定更明智的水资源管理策略的宝贵工具，特别是在气候变化背景下。
